论文标题
重新访问遥感图像中半监督变更检测的一致性正则化
Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images
论文作者
论文摘要
遥感（RS）更改检测（CD）旨在检测共同注册的双期图像的“关注变化”。现有的深层监督CD方法的性能归因于用于训练网络的大量注释数据。但是，注释大量的遥感图像是劳动密集型且昂贵的，尤其是使用双时间段图像，因为它需要人类专家的像素对比较。另一方面，由于越来越多的地球观察计划，我们通常可以使用无限制的无标记的多阶梯RS图像。在本文中，我们提出了一种简单而有效的方法，以利用未标记的双阶段图像的信息来提高CD方法的性能。更具体地说，我们提出了一个半监督的CD模型，在该模型中，除了限制给定未标记的双期内图像对的输出变化概率图外，我们还制定了无监督的CD损失（CE）损失（CE）损失，以使其在深度特征差异图上均能依赖于其latents特征，以在深度特征差异映射下依赖，以使其在深度特征差异上进行。在两个公开可用的CD数据集上进行的实验表明，即使访问带注释的培训数据的10％，提议的半监督CD方法也可以更接近监督CD的性能。代码可在https://github.com/wgcban/spoomd上找到
Remote-sensing (RS) Change Detection (CD) aims to detect "changes of interest" from co-registered bi-temporal images. The performance of existing deep supervised CD methods is attributed to the large amounts of annotated data used to train the networks. However, annotating large amounts of remote sensing images is labor-intensive and expensive, particularly with bi-temporal images, as it requires pixel-wise comparisons by a human expert. On the other hand, we often have access to unlimited unlabeled multi-temporal RS imagery thanks to ever-increasing earth observation programs. In this paper, we propose a simple yet effective way to leverage the information from unlabeled bi-temporal images to improve the performance of CD approaches. More specifically, we propose a semi-supervised CD model in which we formulate an unsupervised CD loss in addition to the supervised Cross-Entropy (CE) loss by constraining the output change probability map of a given unlabeled bi-temporal image pair to be consistent under the small random perturbations applied on the deep feature difference map that is obtained by subtracting their latent feature representations. Experiments conducted on two publicly available CD datasets show that the proposed semi-supervised CD method can reach closer to the performance of supervised CD even with access to as little as 10% of the annotated training data. Code available at https://github.com/wgcban/SemiCD
